METHODS AND RESULT: The pure culture of K. nataicola was obtained from yeast-glucose-calcium carbonate (YGC) agar, followed by genomic DNA extraction, and subjected to whole genome sequencing on a Nanopore flongle flow cell. The genome of K. nataicola consists of a 3,767,936 bp chromosome with six contigs and 4,557 protein coding sequences. The maximum likelihood phylogenetic tree and average nucleotide identity analysis confirmed that the bacterial isolate was K. nataicola. The gene annotation via RAST server discovered the presence of cellulose synthase, along with three genes associated with lactate utilization and eight genes involved in lactate fermentation that could potentially contribute to the increase in acid concentration during BC synthesis.
CONCLUSION: A more comprehensive genome study of K. nataicola may shed light into biological pathway in BC productivity as well as benefit the analysis of metabolites generated and understanding of biological and chemical interactions in BC production later.
MATERIAL AND METHODS: A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression.
RESULTS: Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187).
CONCLUSION: Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.
OBJECTIVE: We sought to determine whether a novel mHealth intervention is useful in enhancing antihypertensive therapy adherence and treatment outcomes among patients with hypertension in a low- to middle-income country.
METHODS: A 6-month parallel, single-blinded, superiority randomized controlled trial recruited 439 patients with hypertension with poor adherence to antihypertensive therapy and access to smartphones. An innovative, multifaceted mHealth intervention (Multi-Aid-Package), based on the Health Belief Model and containing reminders (written, audio, visual), infographics, video clips, educational content, and 24/7 individual support, was developed for the intervention group; the control group received standard care. The primary outcome was self-reported medication adherence measured using the Self-Efficacy for Appropriate Medication Adherence Scale (SEAMS) and pill counting; the secondary outcome was systolic blood pressure (SBP) change. Both outcomes were evaluated at baseline and 6 months. Technology acceptance feedback was also assessed at the end of the study. A generalized estimating equation was used to control the covariates associated with the probability of affecting adherence to antihypertensive medication.
RESULTS: Of 439 participants, 423 (96.4%) completed the study. At 6 months post intervention, the median SEAMS score was statistically significantly higher in the intervention group compared to the controls (median 32, IQR 11 vs median 21, IQR 6; U=10,490, P